Sensitivity Correction Method for the Lunar Soil Volatile Measuring Instrument on Chang’e-7 Considering Binary Gas Mixture

The Lunar Soil Volatile Measuring Instrument, a key payload of the Chang’e-7 mission, employs a quadrupole mass spectrometer (QMS) to directly analyze gases released from lunar regolith at different temperatures, aiming to determine the types and abundances of volatiles. However, the evolved gases are often complex mixtures, and their direct introduction into the mass spectrometer may compromise the measurement accuracy due to interactions among different species. To investigate the interference from gases on volatile quantification, systematic experiments were performed with one or two gases out of H2, He, N2, Ar, CO2, and CO on a flight-like laboratory unit of the payload. Results show that the QMS exhibited excellent reproducibility and linear response (R2 > 0.99) for all pure gases tested. Furthermore, the sensitivity of gases varied in mixtures and was jointly influenced by gas composition and volume fraction. For instance, compared with the sensitivity values obtained in pure gas measurements, the sensitivity of CO was slightly enhanced when mixed with Ar but was reduced when mixed with H2 or He. A significant sensitivity enhancement of up to 4.6 folds was observed for H2 when mixed with He. However, as its fraction increased, the sensitivity of a component in a binary mixture exhibited a decreasing deviation and was almost constant when its fraction was above 60%. Based on these findings, we developed a sensitivity correction method which employs an iterative algorithm to obtain more accurate partial pressures calculated from the gas measurement signals. Applications of the method on H2–He mixtures and a pre-mixed CO–N2 standard gas demonstrated that the relative errors of calculated pressures can be reduced to within ±10%. This method would significantly improve the accuracy of gas pressure calculated from in situ volatile measurement data and also provides a valuable reference for similar QMSs.